Atlas-driven lung lobe segmentation in volumetric x-ray CT images

The positions of the lobar fissures are of growing interest as computer-based quantitative measures to detect early pathologies and to predict or measure outcomes emerge. While we have developed a semi-automatic fissure detection method in our previous work, in this paper we, describe the use of an anatomic pulmonary atlas with a priori knowledge about lobar fissures to automatically segment the lobar fissures. 16 volumetric CT scans from 16 subjects are used to construct the pulmonary atlas. After deforming the fissures onto a template image, the average fissure and variability between different subjects can be obtained by local statistical measures. The probabilistic analysis for the atlas shows that the atlas can provide an initialization for the fissure detection in certain regions with a predictable variation, although the initialization may not be close and complete. A ridgeness measure is applied on original images to enhance the fissure contrast. The fissure detection is accomplished by the initial fissure search. and the final fissure search. While only parts of the initial search results are correctly delineated, a regional statistic analysis of ridgeness selects the most "reliable" initial search results, which are then used to initialize the final search. Our method has been tested in 22 volumetric thin-slice CT images from 12 subjects, and the results are compared to manual tracings. The mean of the similarity indices between the manual and computer defined lobes is 0.988. The results indicate a strong agreement between the automatic and manual lobe segmentations.